本文選題：Hu不變矩 + 磁共振��； 參考：《南京師范大學(xué)》2017年碩士論文

【摘要】：(1)目的:本文首先介紹了研究背景及意義,然后對(duì)磁共振(MR)圖像診斷的國內(nèi)外發(fā)展現(xiàn)狀做了簡單的介紹。本文所提出的智能病腦檢測系統(tǒng)(SPBD)即是一種計(jì)算機(jī)智能輔助診斷病理核磁共振圖像系統(tǒng)。人工智能算法的研究將有助于提高檢測分類的效率和準(zhǔn)確率,在病腦檢測領(lǐng)域具有十分重要的意義。本文采用神經(jīng)網(wǎng)絡(luò)與MR圖像相結(jié)合的思路。由于神經(jīng)網(wǎng)絡(luò)在分類訓(xùn)練中,數(shù)據(jù)容易陷入局部最優(yōu)。所以本文采用了一種較新的,非常有效的捕食者-食餌粒子群算法(PP-PSO)來優(yōu)化神經(jīng)網(wǎng)絡(luò),從而避免了數(shù)據(jù)易陷入局部最優(yōu)問題,增強(qiáng)了SPBD系統(tǒng)的對(duì)新數(shù)據(jù)處理、分類的能力,實(shí)現(xiàn)了 SPBD系統(tǒng)對(duì)病腦檢測的高效,高準(zhǔn)確率。(2)方法:本文采用DA-160數(shù)據(jù)樣本,采用Hu不變矩(HMI)來提取腦圖像特征,Hu不變矩具有平移、旋轉(zhuǎn)、比例不變性,在目標(biāo)識(shí)別、圖像匹配、形狀分析等領(lǐng)域都有廣泛的應(yīng)用。本文采用單隱層神經(jīng)網(wǎng)絡(luò)(SLN)作為分類器。人工神經(jīng)網(wǎng)絡(luò)(ANN)通過模仿人腦形象思維構(gòu)建神經(jīng)網(wǎng)絡(luò),從而實(shí)現(xiàn)分布式的信息處理,具有良好的自適應(yīng)、自組織和很強(qiáng)的自學(xué)能力,是數(shù)據(jù)分類圖像識(shí)別的有力工具。用HMI提取得到的一系列由七個(gè)特征矩組成的矩陣信息輸入SLN,經(jīng)過SLN訓(xùn)練,輸出的結(jié)果為非0即1的信息(0表示健康大腦圖像,1表示病腦圖像)。為了使實(shí)驗(yàn)不易陷入局部最優(yōu)解,本文采用了一種基于粒子群算法(PSO)改進(jìn)的優(yōu)化算法——捕食者-食餌粒子群優(yōu)化算法(PP-PSO)來訓(xùn)練SLN的權(quán)值。我們將采用五折分層交叉驗(yàn)證(FFSCV)來對(duì)數(shù)據(jù)進(jìn)行訓(xùn)練,從而保證了對(duì)有限數(shù)據(jù)集進(jìn)行盡可能多的學(xué)習(xí)。最后使用分類準(zhǔn)確率作為實(shí)驗(yàn)優(yōu)良的評(píng)判標(biāo)準(zhǔn)。(3)結(jié)果:將實(shí)驗(yàn)結(jié)果與其他六種較先進(jìn)的SPBD算法進(jìn)行比較,通過訓(xùn)練輸出結(jié)果對(duì)比,發(fā)現(xiàn)本文的方法,基于捕食者一食餌粒子群算法和單隱層神經(jīng)網(wǎng)絡(luò)算法(HMI + SLN + PP-PSO)分類效果最好,對(duì)160個(gè)數(shù)據(jù)集進(jìn)行測試,靈敏度、特征度和準(zhǔn)確率分別達(dá)到了: 96.00±5.16%,98.57±0.75%和98.25±0.65%。最后比較了 PSO和PP-PSO分別對(duì)應(yīng)的準(zhǔn)確率。其中,PSO作為該實(shí)驗(yàn)的優(yōu)化算法準(zhǔn)確率達(dá)到96.44%。(4)結(jié)論:比較發(fā)現(xiàn),HMI + SLN + PP-PSO分類性能最好,實(shí)驗(yàn)結(jié)果準(zhǔn)確率最高。而且,通過實(shí)驗(yàn)結(jié)果的比較分析能發(fā)現(xiàn)HMI + SLN + PP-PSO方法的優(yōu)勢(shì)和不足,為SPBD更進(jìn)一步的研究和優(yōu)化做了鋪墊。
[Abstract]:Objective: this paper first introduces the background and significance of the research, and then briefly introduces the development of MRI imaging diagnosis at home and abroad.The intelligent brain detection system (SPBDD) proposed in this paper is a computerized intelligent diagnostic system for patho-magnetic resonance imaging (MRI).The research of artificial intelligence algorithm will help to improve the efficiency and accuracy of detection and classification, which is of great significance in the field of brain disease detection.In this paper, the idea of combining neural network with Mr image is adopted.Because the neural network in the classification training, the data is easy to fall into the local optimum.So we use a new and very effective predator-prey particle swarm optimization algorithm (PP-PSO) to optimize the neural network, which avoids the data falling into the local optimal problem and enhances the ability of SPBD system to process and classify the new data.The method of high efficiency and high accuracy of SPBD system for detecting diseased brain is realized. In this paper, DA-160 data sample and Hu invariant moment are used to extract the feature of brain image. Hu invariant moment has translation, rotation, scale invariance, and is used in target recognition.Image matching, shape analysis and other fields have been widely used.In this paper, single hidden layer neural network (SLN) is used as classifier.Artificial neural network (Ann) is a powerful tool for data classification and image recognition, which can construct neural network by imitating human brain image thinking, thus realizing distributed information processing, with good self-adaptation, self-organization and strong self-learning ability.A series of matrix information, which is composed of seven characteristic moments, was extracted by HMI. After SLN training, the output result is that the information of non-zero or 1 represents the healthy brain image / 1 to represent the diseased brain image.In order to make the experiment difficult to fall into the local optimal solution, an improved particle swarm optimization algorithm based on particle swarm optimization (PSO), Predator-prey PSO (Predator-Prey PSO), is used to train the weight of SLN.We will use the FFSCV to train the data, so that we can learn as much as possible from the limited data set.Finally, the classification accuracy rate is used as the excellent criterion of the experiment. The results are compared with the other six advanced SPBD algorithms, and the method of this paper is found by comparing the results of the training output with those of the other six advanced SPBD algorithms.Based on predator-prey particle swarm optimization algorithm and single hidden layer neural network algorithm, HMI SLN PP-PSO-based classification is the best. The sensitivity, characteristic and accuracy of 160 data sets are 96.00 鹵5.1610 鹵0.75% and 98.25 鹵0.65%, respectively.Finally, the accuracy of PSO and PP-PSO are compared.Conclusion: the comparison shows that the classification performance of HMI SLN PP-PSO is the best, and the accuracy of experimental results is the highest.Furthermore, the advantages and disadvantages of the HMI SLN PP-PSO method can be found by comparing the experimental results, which pave the way for the further research and optimization of SPBD.
【學(xué)位授予單位】：南京師范大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：R741.044;TP18

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 滿玉琳;郭佑民;楊靜;尤葆華;;低場磁共振對(duì)膝關(guān)節(jié)軟骨損傷評(píng)價(jià)的Meta分析[J];影像技術(shù);2017年02期

2 王全;包禮杰;孫臣義;宋長悅;申正坤;;BPPV診斷中內(nèi)耳磁共振水成像的應(yīng)用價(jià)值[J];中國醫(yī)療設(shè)備;2017年03期

3 王寧;周圓;劉敬浩;;一種基于改進(jìn)粒子群的無線傳感器網(wǎng)絡(luò)層次化聚類協(xié)議[J];傳感技術(shù)學(xué)報(bào);2017年01期

4 李金華;張建李;姚芳萍;蘇智超;;粒子群算法在提高激光彎曲中神經(jīng)網(wǎng)絡(luò)泛化性上的應(yīng)用[J];熱加工工藝;2016年21期

5 胡鐵松;嚴(yán)銘;趙萌;;基于領(lǐng)域知識(shí)的神經(jīng)網(wǎng)絡(luò)泛化性能研究進(jìn)展[J];武漢大學(xué)學(xué)報(bào)(工學(xué)版);2016年03期

6 韓廣;徐璐;孫曉云;盧兆楠;田家輝;;空氣污染指數(shù)的前饋神經(jīng)網(wǎng)絡(luò)預(yù)測方法[J];計(jì)算機(jī)與應(yīng)用化學(xué);2016年02期

7 趙銀平;張剛林;溫陽東;甘敏;;基于VP算法的前饋神經(jīng)網(wǎng)絡(luò)參數(shù)優(yōu)化[J];控制工程;2016年02期

8 游佳麗;周志勇;章程;戴亞康;;基于自適應(yīng)驅(qū)散機(jī)制的粒子群優(yōu)化算法[J];計(jì)算機(jī)工程與應(yīng)用;2017年07期

9 王水花;張煜東;楊建飛;施建平;;利用三維腦核磁共振圖像與RBF核支持向量機(jī)檢測人腦輕度認(rèn)知障礙[J];合肥工業(yè)大學(xué)學(xué)報(bào)(自然科學(xué)版);2015年10期

10 王水花;張煜東;;壓縮感知磁共振成像技術(shù)綜述[J];中國醫(yī)學(xué)物理學(xué)雜志;2015年02期

，

本文編號(hào)：1767069